In recent years, Multi-Object Tracking (MOT) has gained increased attention due to its potential applications in traffic and person detection. We have observed that in most tracking scenarios, objects tend to move and be lost within specific locations. To address this, we propose different strategies for tracking and association that can identify and target these regions. Additionally, we note that tracking by detection may be impacted by errors in the detector, such as an imprecise bounding box. To counter this, we present a robust strategy for dealing with lost objects, as well as a location-wise method for tracking by detection that includes three improvements in lost tracklet management. Resulting Mesh-SORT, it gives mesh division for the original frame, and applying strategies for differentiation. Experiments demonstrate the potential of our approach and the improvements it provides over the baseline.

翻译：近年来，多目标跟踪（MOT）因其在交通和人员检测中的潜在应用而受到越来越多的关注。我们观察到，在大多数跟踪场景中，目标倾向于在特定位置移动和丢失。为了解决这一问题，我们提出了不同的跟踪与关联策略，以识别并定位这些区域。此外，我们注意到基于检测的跟踪可能受到检测器错误的影响，例如不精确的边界框。为此，我们提出了一种处理丢失目标的鲁棒策略，以及一种基于位置的检测跟踪方法，该方法在丢失轨迹管理方面包含三项改进。由此产生的Mesh-SORT对原始帧进行网格划分，并应用差异化策略。实验证明了我们方法的潜力及其相较于基线模型的改进效果。